Here we model an intervention to address malnutrition among school-aged children in urban Hanoi. The gardens will be existing areas on school grounds. The STEM option gardens will be designed to for teaching about nutrition and STEM (an approach to learning and development that integrates science, technology, engineering and maths) at primary and secondary schools.
The model was developed across several iterative workshops in July 2023. These included decision definition, conceptual model development of the selected decision and an initial programming session with preliminary model results. The resulting model was further developed through August to November 2023. A test run of the intervention will be carried out by the Center for the Development of Organic Agriculture (CODAS) under the Association of Organic Agriculture of Vietnam. In the 2nd year the garden is expected to start running well. The 3rd year is when the STEM education plan will be fully running.
This is the link to our simulation of the school garden intervemntion options.
# Source our model
source("CODAS_Garden_Model.R")
##
## Attaching package: 'decisionSupport'
## The following objects are masked _by_ '.GlobalEnv':
##
## chance_event, discount, vv
## Warning: Variable: outside_investment_value distribution: posnorm
## Calculated value of 5%-quantile: 1.77932900574854
## Target value of 5%-quantile: 1
## Calculated cumulative probability at value 1 : 0.0289226319595083
## Target cumulative probability at value 1 : 0.05
## Mean scaled difference: 0.4215474
## Warning in paramtnormci_numeric(p = p, ci = ci, lowerTrunc = lowerTrunc, : Calculated value of 5%-quantile: 1.77932900574854
## Target value of 5%-quantile: 1
## Calculated cumulative probability at value 1 : 0.0289226319595083
## Target cumulative probability at value 1 : 0.05
## Mean scaled difference: 0.4215474
# Ensure consistent results with the random number generator
# not for each 'run' of the MC simulation but for
# consistency each time we call on the simulation
set.seed(1234)
garden_simulation_results <- mcSimulation(
estimate = estimate_read_csv("inputs_school_garden.csv"),
model_function = school_garden_function,
numberOfModelRuns = 1000, #run 1000 times
functionSyntax = "plainNames"
)
## Warning: Variable: outside_investment_value distribution: posnorm
## Calculated value of 5%-quantile: 1.77932900574854
## Target value of 5%-quantile: 1
## Calculated cumulative probability at value 1 : 0.0289226319595083
## Target cumulative probability at value 1 : 0.05
## Mean scaled difference: 0.4215474
## Warning: Calculated value of 5%-quantile: 1.77932900574854
## Target value of 5%-quantile: 1
## Calculated cumulative probability at value 1 : 0.0289226319595083
## Target cumulative probability at value 1 : 0.05
## Mean scaled difference: 0.4215474
Here is a plot of the Net Present Value (i.e. current value of the
future benefits) of the three options. "NPV_garden" is the
value of the 5 years of the garden
intervention."NPV_garden_STEM" is the same garden but with
the additional costs and benefits of a full STEM education program,
"NPV_no_garden" is the result of 5 years of using the land
for something that is not related to the garden, i.e. as a playground or
for parking:
source("functions/plot_distributions.R")
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
plot_distributions(mcSimulation_object = garden_simulation_results,
vars = c("NPV_garden","NPV_garden_STEM", "NPV_no_garden"),
method = 'hist_simple_overlay',
base_size = 7,
x_axis_name = "Comparative NPV outcomes")
Under Prospect Theory the way we present NPV values can influence decision makers - the same information presented in different ways can lead to different decisions. For example, framing a projected NPV gain as a “reduction in potential loss” might make it more attractive to decision makers due to loss aversion.
Here we plot the distribution for the decision and frame the
projected NPV gain for the ‘decision’. These are distributions for the
options, with the NPV values of the no garden option subtracted from
those for the garden, the decision and garden with STEM,
the decision_STEM. We display this as a “Reduction in
potential loss” as it is expected to be more attractive to decision
makers due to loss aversion, i.e. school boards might put more emphasis
on avoiding potential losses than on seeking gains. We can frame our
results as a strategy that minimizes losses rather than one that
maximizes gains.
plot_distributions(mcSimulation_object = garden_simulation_results,
vars = c("decision", "decision_STEM"),
method = 'hist_simple_overlay',
base_size = 7,
x_axis_name = "Reduction in potential loss")
Here we provide a summary of the garden intervention options with
gt_plt_summary() from {gtExtras} and with options from
{svglite}.
# Subset the outputs from the mcSimulation function (y) to summarize only on the variables that we want.
# names(garden_simulation_results$x)
mcSimulation_summary <- data.frame(garden_simulation_results$x[2:61],
# names(garden_simulation_results$x)
garden_simulation_results$y[1:7])
gt_plt_summary(mcSimulation_summary)
| mcSimulation_summary | ||||||
| 1000 rows x 67 cols | ||||||
| Column | Plot Overview | Missing | Mean | Median | SD | |
|---|---|---|---|---|---|---|
| discount_rate | 0.0% | 6.5 | 6.5 | 0.9 | ||
| size_of_garden | 0.0% | 75.2 | 75.2 | 14.9 | ||
| CV_value | 0.0% | 0.3 | 0.3 | 0.1 | ||
| inflation_rate | 0.0% | 7.5 | 7.5 | 1.5 | ||
| if_students_like | 0.0% | 0.6 | 0.6 | 0.1 | ||
| if_parents_like | 0.0% | 0.7 | 0.7 | 0.1 | ||
| if_community_likes | 0.0% | 0.5 | 0.5 | 0.2 | ||
| if_effective_manage | 0.0% | 0.6 | 0.6 | 0.1 | ||
| if_garden_yield_enough | 0.0% | 0.5 | 0.5 | 0.1 | ||
| if_garden_healthy | 0.0% | 0.7 | 0.7 | 0.1 | ||
| if_teachers_like | 0.0% | 0.5 | 0.5 | 0.2 | ||
| if_effective_teaching | 0.0% | 0.6 | 0.6 | 0.2 | ||
| if_effective_training | 0.0% | 0.5 | 0.5 | 0.2 | ||
| if_offer_green_space | 0.0% | 0.7 | 0.7 | 0.1 | ||
| if_reduce_polution | 0.0% | 0.3 | 0.3 | 0.1 | ||
| if_biophysical_good | 0.0% | 0.3 | 0.3 | 0.1 | ||
| equipment_cost | 0.0% | 74.4 | 74.8 | 15.6 | ||
| construction_cost | 0.0% | 22.6 | 22.6 | 4.8 | ||
| garden_designing_costs | 0.0% | 12.5 | 12.5 | 1.6 | ||
| teacher_training_cost | 0.0% | 12.5 | 12.7 | 4.5 | ||
| school_board_planning | 0.0% | 9.0 | 9.0 | 1.8 | ||
| teaching_equipment | 0.0% | 7.6 | 7.5 | 1.5 | ||
| compost_starting | 0.0% | 7.5 | 7.5 | 1.5 | ||
| worm_starting | 0.0% | 3.4 | 3.4 | 0.9 | ||
| livestock_costs | 0.0% | 3.5 | 3.5 | 0.9 | ||
| if_family_pays_establishment | 0.0% | 0.3 | 0.4 | 0.1 | ||
| establishment_family_portion_paid | 0.0% | 0.3 | 0.3 | 0.1 | ||
| maintaining_labor | 0.0% | 32.6 | 32.4 | 4.6 | ||
| teacher_salary_cost | 0.0% | 24.9 | 25.0 | 3.1 | ||
| teaching_equipment_annual | 0.0% | 7.6 | 7.6 | 1.5 | ||
| teaching_tools | 0.0% | 3.5 | 3.5 | 0.9 | ||
| seed_costs | 0.0% | 1.5 | 1.5 | 0.3 | ||
| fertilizer | 0.0% | 1.5 | 1.5 | 0.3 | ||
| plant_protection | 0.0% | 3.5 | 3.6 | 0.9 | ||
| livestock_maint | 0.0% | 6.0 | 6.1 | 2.4 | ||
| annual_teacher_training | 0.0% | 4.0 | 4.0 | 0.6 | ||
| if_school_has_canteen | 0.0% | 0.3 | 0.3 | 0.1 | ||
| canteen_savings | 0.0% | 7.5 | 7.5 | 1.6 | ||
| sale_of_yield | 0.0% | 20.1 | 20.1 | 6.0 | ||
| extra_cirricular_savings | 0.0% | 60.9 | 61.6 | 24.4 | ||
| formal_edu_savings | 0.0% | 9.2 | 8.8 | 5.8 | ||
| formal_edu_savings_STEM | 0.0% | 60.4 | 60.1 | 24.6 | ||
| outside_investment_value | 0.0% | 33.0 | 23.4 | 32.5 | ||
| outside_investment_value_STEM | 0.0% | 170.0 | 125.1 | 155.6 | ||
| increased_enrollment_value | 0.0% | 9.3 | 8.3 | 6.0 | ||
| increased_enrollment_value_STEM | 0.0% | 50.8 | 49.6 | 27.2 | ||
| if_increase_tuition | 0.0% | 0.0 | 0.0 | 0.0 | ||
| if_increase_tuition_STEM | 0.0% | 0.2 | 0.2 | 0.1 | ||
| tuition_increase | 0.0% | 7.5 | 7.5 | 1.6 | ||
| child_veg_access | 0.0% | 7.5 | 7.5 | 1.5 | ||
| child_healthier_choices | 0.0% | 58.3 | 57.2 | 23.8 | ||
| child_healthier_choices_STEM | 0.0% | 298.1 | 291.7 | 125.4 | ||
| green_space_value | 0.0% | 149.5 | 151.0 | 29.4 | ||
| reduce_polution_value | 0.0% | 15.0 | 14.9 | 3.1 | ||
| school_event_value | 0.0% | 29.6 | 29.5 | 12.0 | ||
| school_event_freq | 0.0% | 6.1 | 6.0 | 2.5 | ||
| value_of_non_garden_land_use | 0.0% | 35.5 | 35.4 | 9.3 | ||
| if_parking | 0.0% | 0.1 | 0.1 | 0.0 | ||
| parking_value | 0.0% | 149.2 | 147.2 | 30.2 | ||
| costs_of_non_garden_land_use | 0.0% | 3.0 | 2.9 | 1.2 | ||
| NPV_garden | 0.0% | 621.5 | 550.3 | 355.1 | ||
| NPV_garden_STEM | 0.0% | 1,399.2 | 1,302.6 | 645.7 | ||
| NPV_no_garden | 0.0% | 327.5 | 247.4 | 277.6 | ||
| decision | 0.0% | 294.0 | 290.6 | 455.4 | ||
| decision_STEM | 0.0% | 1,071.7 | 985.4 | 707.5 | ||
| total_costs | 0.0% | 412.8 | 412.4 | 36.3 | ||
| total_costs_STEM | 0.0% | 642.7 | 644.1 | 43.4 | ||
Summary of the savings for the passive education garden option
summary(garden_simulation_results$y$decision)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1499.07 56.25 290.57 294.05 542.85 2376.05
Summary of the savings for the formal STEM education garden option
summary(garden_simulation_results$y$decision_STEM)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1131.7 600.4 985.4 1071.7 1459.6 4192.8
Summary of costs
summary(garden_simulation_results$y$total_costs)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 266.6 389.5 412.4 412.8 437.0 562.3
summary(garden_simulation_results$y$total_costs_STEM)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 503.1 611.8 644.1 642.7 670.6 816.3
summary(garden_simulation_results$y$Cashflow_garden1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -135.330 -36.560 3.442 13.786 55.717 382.954
summary(garden_simulation_results$y$Cashflow_garden_STEM1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -168.02 35.18 110.95 126.73 194.57 699.93
source("functions/plot_cashflow.R")
plot_cashflow(mcSimulation_object = garden_simulation_results,
cashflow_var_name = "Cashflow_garden")
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning: Removed 5 rows containing missing values (`geom_line()`).
## Removed 5 rows containing missing values (`geom_line()`).
Cashflow of the garden option with formal STEM education
source("functions/plot_cashflow.R")
plot_cashflow(mcSimulation_object = garden_simulation_results,
cashflow_var_name = "Cashflow_garden_STEM")
Here we assess value of information with the multi_EVPI
function.
# Subset the outputs from the mcSimulation function (y) by selecting the correct variables be sure to run the multi_EVPI only on the variables that we want. Find them with names(garden_simulation_results$y)
mcSimulation_table <- data.frame(garden_simulation_results$x,
garden_simulation_results$y[1:5])
Value of information for the garden option (no STEM).
source("functions/multi_EVPI.R")
# first_out_var is the first result variable in the table, "NPV_garden" in our case.
evpi <- multi_EVPI(mc = mcSimulation_table, first_out_var = "NPV_garden")
## [1] "Processing 5 output variables. This can take some time."
## [1] "Output variable 1 (NPV_garden) completed."
## [1] "Output variable 2 (NPV_garden_STEM) completed."
## [1] "Output variable 3 (NPV_no_garden) completed."
## [1] "Output variable 4 (decision) completed."
## [1] "Output variable 5 (decision_STEM) completed."
source("functions/plot_evpi.R")
plot_evpi(evpi, decision_vars = "decision")
Value of information for the garden option with formal STEM
education.
# using the results of the same multi_EVPI
plot_evpi(evpi, decision_vars = "decision_STEM")
## Warning: There are no variables with a positive EVPI. You probably do not need
## a plot for that.
We use Projection to Latent Structures model to get some sense of the correlation strength and direction for model variables and our outcome variables.
For passive education garden option
source("functions/pls_model.R")
pls_result <- pls_model(object = garden_simulation_results,
resultName = names(garden_simulation_results$y)[1], # the "NPV_garden"
ncomp = 1)
# read in the common input table
input_table <- read.csv("inputs_school_garden.csv")
# source the plot function
source("functions/plot_pls.R")
plot_pls(pls_result, input_table = input_table, threshold = 0.9)
For school garden with formal STEM education
pls_result_STEM <- pls_model(object = garden_simulation_results,
resultName = names(garden_simulation_results$y)[2], # the "NPV_garden_STEM"
ncomp = 1)
plot_pls(pls_result_STEM, input_table = input_table, threshold = 0.9)
The full repository can be accessed at https://github.com/CWWhitney/nifam_codas_school_garden with the following QR code.